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ISTD PhD Oral Defence Seminar by Chia Yew Ken – This thesis investigates the crucial role of structured information in natural language processing and artificial intelligence, with a focus on its extraction, utilisation, and extension to multimodal reasoning.

ISTD PhD Oral Defence Seminar by Bhardwaj Rishabh – This thesis investigates critical non-idealities in AI systems, focusing on safety behaviour post-training and alignment.

Congratulations to PhD Student Garbelini Matheus Eduardo for winning the Intel Bug Bounty Award – Information Systems Technology and Design (ISTD)

Congratulations to PhD student Tok Yee Ching for his win at The Cybersecurity Awards 2019 – Information Systems Technology and Design (ISTD)

He was honored with the “Winner of RSC Excellent Student Award” by the Royal Society of Chemistry (RSC) this September. This award recognizes his academic excellence in leveraging computational chemistry to facilitate interdisciplinary research and his contribution to promoting diversity, equity, inclusion, and respect (DEIR) within the scientific community.

ISTD PhD Oral Defense Seminar by Haoran Li – Language models are critical to the advancement of natural language processing and general artificial intelligence. In this thesis, we aim to address the limitations of language models, particularly focusing on the exposure bias in Autoregressive (AR) models and the label bias in Non-Autoregressive (NAR) models.

ISTD PhD Oral Defence Seminar by Teo Tzu Hsuan Christopher – In this dissertation, we make important contributions in improving fairness in generative models by identifying and addressing constraints which may limit their broader adoption.

Congratulations to Associate Professor Liu Xiaogang’s PhD Student in winning the Royal Society of Chemistry (RSC) Excellent Student Award

ISTD PhD Oral Defense Seminar by Li Xu – In the ever-evolving field of artificial intelligence (AI), deep learning has emerged as a pivotal technique driving remarkable advancements across various domains. Among its many branches, multi-modal learning stands out as a particularly significant approach, which involves integrating and processing information from multiple modalities of data, such as visual content and language information, to enhance the capabilities of AI systems.

ISTD PhD Oral Defence Seminar by Wang Jiazhao – The increase of the Internet of Things (IoT) has created a complex and heterogeneous wireless ecosystem, demanding IoT gateways that are both flexible and efficient. While Software Defined Radio (SDR) provides the necessary hardware adaptability, its potential is frequently undermined by significant software implementation challenges, including a lack of portability across platforms, prohibitive design complexity for advanced algorithms, and poor computational efficiency. This thesis posits that these persistent bottlenecks can be overcome by a paradigm shift in physical layer (PHY) design: reframing core communication functionalities as learnable, interpretable neural network (NN) models.